Multivariate Analysis of Students’ Perception of the Impact of Lecturers’ Ranks on their Performance at the Faculty of Mathematical Sciences, University for Development Studies-Navrongo, Ghana
Bibliographic record
Abstract
This paper examines students’ perception of the impact of lecturers’ ranks on their performance across departments in the Faculty of Mathematical Sciences (FMS) of the University for Development Studies (UDS), Navrongo Campus. The study used a self-designed structured questionnaire administered to 160 respondents (students) of the Faculty. All the 160 questionnaires were retrieved, which represents 100% response rate. The data were analyzed using Statistical Package for Social Sciences (SPSS) version 25.0 for windows. Multivariate Analysis of Variance (MANOVA) results showed that (at P<0.05) Senior Lecturers received higher ratings followed by Lecturers and then Assistant Lecturers, indicating that the ranks of Teaching Staff significantly influenced their performance across the various departments of the Faculty. Recommendations and implications for management of Higher Institutions of Learning (HIL) have been discussed. The paper contributes to the literature in the area of supervision and evaluation of the performance of teaching staff in the HIL context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".